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Strategically Targeted Academic Research On Sensor Networking and.

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Presentation on theme: "Strategically Targeted Academic Research On Sensor Networking and."— Presentation transcript:

1 Strategically Targeted Academic Research On Sensor Networking and Signal Processing for Smart and Safe Buildings Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University 121 Link Hall Syracuse, New York USA

2 2 Overall Structure of the Center Strategically Targeted Academic Research 9 Academic Institutions 2 not-for-profit Research institutes Technology Transfer 50 Corporate Partners Fosters University/Industry collaboration Regional Partnership of Industry & Academe Strategically Targeted Academic Research Technology Transfer and Commercialization

3 3 Centers Hub and Distributed Facilities

4 4 Outline o Introduction o Key challenges and issues o Illustrative examples o Concluding remarks

5 5 Indoor Air Pollution SEALED WINDOWS No access to outdoor air CARCINOGENIC PRODUCTS 70,000 chemical cleaning products on the market COPY MACHINE AND PRINTERS Emit Ozone THE OFFICE BATHROOM Mold machine BUILDING RENOVATIONS Paint fumes, dust, odors PEOPLE AND FURNITURE Paint, carpet emit VOCs Clothes/Grooming Products SMOKING Circulates through the ventilation system EXTERMINATORS Pesticides contain carcinogens WHAT FRESH AIR? Vents located over loading docks Do you work in a Toxin Factory?* *Business Week June 5, 2000

6 6 Societal and Economic Drivers o Health u 17.7 million asthma cases (4.8 million children) u thousand annual deaths due to elevated levels of particulate matter o Productivity u $40 to $250 billion productivity loss due to poor IEQ o Sustainability u $110 billion annual economic loss due to air pollution in urban areas u 40% of total building energy consumption is for environmental control (over 15% of total US energy consumption) o Security u Built and urban environments are vulnerable to chemical/biological threats

7 7 The Problem o Wide spectrum of buildings u Residences, schools, hospitals, apartment buildings, office buildings, factories, high-valued assets o Indoor air quality goals u Health u Productivity u Exposure and risk u Energy consumption cost o Scenarios u Routine day-to-day FHealth, productivity, costs FTime to react is not critical u Emergency FSafety, exposure FRapid response required o Affordability and cost issues u New Buildings u Retrofit

8 8 The Problem o Some current solutions u A single thermal sensor FUneven/asymmetric conditions Finefficient u Provide multiple knobs FControl system is not adequate u Replace indoor air by fresh air frequently FToo costly u Hybrid and demand-controlled ventilation FUse sensing and control FMaximize benefits of natural driving forces FControl needed due to changing weather conditions

9 9 Motivation o These and other current solutions are fairly primitive! o They use one size fits all solutions and do not reduce human exposure and maximize comfort to the desirable extent o Due to a wide spectrum of buildings and their scales, multiplicity of goals, and response time requirements, intelligent solutions are required!

10 10 Why Distributed Large-scale Wireless Sensor Networks? o Higher resolution and fidelity data available in a sensor- rich environment for customized environments u Improved IAQ at different scales, e.g., personal level, thus increasing productivity without much increase in cost u Rapid response in emergency situations u Improved reliability and robustness u More degrees of freedom for distributed control o Enabling technologies are fairly mature for practical applications

11 11 Conceptual Process Diagram

12 12 Key Components o Sensor Networks u Topology, architecture, protocols and management o Intelligent Information Processing u Information fusion, learning algorithms, and knowledge discovery o Control and Mitigation Methodology u Control worthy models based on reduced order models, hierarchical distributed control, mitigation and evacuation

13 13 Distributed and Pervasive Sensing Paradigm Control/Action Devices Sensor Local Decision Makers Global Decision Maker

14 14 Challenges and Issues in i-EQS Sensor Networks Distribution among wired and wireless sensors is not known Sensor network architecture including topology, number and placement of sensors, and protocols has not been addressed. Resource management including bandwidth and energy management has not been investigated. Security and information assurance requirements are not well understood. Lack of design principles for sensor networks in buildings Challenge 1 Challenge 2 Challenge 3 Challenge 4

15 15 Challenges and Issues in i-EQS Information Processing Inferencing and control mostly based on single sensor measurements. Systems do not take full advantage of networked sensors, information fusion and intelligent signal processing algorithms. Spatial and temporal dimensions (e.g. forecasting) are not explored in detail. Systems are not robust and responsive to evolving dynamic situations. Lack of intelligent information processing algorithms that fully exploit all available information Challenge 1 Challenge 2 Challenge 3 Challenge 4

16 16 Challenges and Issues in i-EQS Control Lack of robust multi-level intelligent model-based control algorithms Event and state recognition with incomplete information Complex, non-linear and state/objective dependent dynamics Slow system response Resources constraints, e.g, sensors, actuators, computing power, bandwidth Challenge 1 Challenge 2 Challenge 3 Challenge 4

17 17 Sensor Placement Problem o Problem: Determining the locations where sensors should be placed, maximizing coverage and detection capability while minimizing cost o Factors and Problem Parameters: u Building layout u Air inlet and outlet (HVAC) locations u Air flow simulation and analytic models u Sensor characteristics and costs o Approach: u Multiobjective optimization u Modeling each candidate configuration of sensors as a point in a multidimensional space u Applying evolutionary algorithms to sample search space effectively and efficiently

18 18 Data Fusion Issues o Problems: u Detecting the presence of activities of interest, e.g., abnormally high pollutant concentration u Classifying the type of activity, e.g., the type of pollutant o Factors and Problem Parameters: u Sensor Characteristics in terms of their detection ability u Sensor location and coverage o Approach u Distributed detection theory – decision fusion u Algorithms to deal with uncertainties – modeling errors, asynchronous information u Adaptation to changing environmental conditions

19 19 Decision Fusion Data fusion center u1u1 u2u2 uNuN... u0u0

20 20 Design of Fusion Rules Input to the fusion center: u i, i=1, …, N Output of the fusion center: u 0 Fusion rule: logical function with N binary inputs and one binary output Number of fusion rules: 2 2 N 0,if detector i decides H 0 1,if detector i decides H 1 u i = 0,if H 0 is decided 1,otherwise u 0 =

21 21 Optimum Decision Fusion The optimum fusion rule that minimizes the probability of error is P. K. Varshney, Distributed Detection and Data Fusion, Springer, 1997

22 22 Inferencing in Distributed Sensor Networks o Problems: u Detecting relationships between pollutant concentrations at different locations u Detecting locations of abnormally high pollutant sources o Factors and Problem Parameters: u Fluid flow models and simulations u Pollutant source models and locations u Potential sensor locations o Approach: u Inferencing with time-sensitive probabilistic (Bayesian) network models

23 23 Illustrative Examples o UC Berkeley study shows that the use of multiple sensors and ad hoc control strategies (Single HVAC) reduced energy consumption as well as predicted percentage dissatisfied (PPD) u Energy-optimal scheme F17% reduction in energy consumption F6% reduction in PPD 30% 24% u Comfort-optimal scheme F4% reduction in energy consumption F10% reduction in PDD 30% 20% N. Lin, C. Federspiel and D. Auslander, Multi-sensor Single-Actuator Control of HVAC Systems, Int. Conf. For Enhanced Building Operations, Richardson, TX, 2002

24 24 Intelligent Control of Building Environmental Systems for Optimal Evacuation Planning by J.S. Zhang 1, C.K. Mohan 2, P. Varshney 2, C. Isik 2, K. Mehrotra 2, S. Wang 1, Z. Gao 1, and R. Rajagopalan 2 1 Dept. of Mechanical, Aerospace and Manufacturing Engineering 2 Dept. of Electrical Engineering and Computer Science Environmental Quality Systems Center ( College of Engineering and Computer Science Syracuse University

25 25 i-BES for Optimal Evacuation Planning Prediction of Pollutant Dispersion Optimization of Peoples Movement Monitoring of BES Conditions Personal Env. Zone/ Room Multizone Building Outdoor Airshed Multi-level Controls: 321 Occupant 0 Simulated Control Operations Predictive control algorithm

26 26 Pollutant Dispersion in a 6-zone testbed Building Energy and Environmental Systems Laboratory (BEESL) at Syracuse University Zone

27 27 Pollutant Dispersion: Multizone Model Simulations c ee e e Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 a a b Turn off Exhaust Fan for the Corridor Zone Pressurization Exhaust Shut off supply air Release at Outdoor Air Intake d d Open exhaust dampers Zone

28 28 Zone Multizone Model Simulation Results Pollutant Dispersion Control and Evacuation Plan Concentration change over time: Evacuation routes:

29 29 A 73-Zone Example (a floor section of 22,000 ft 2 )

30 30 Concluding Remarks o Management of indoor air quality is an interesting and challenging application. o Theory and implementation is in its infancy. o Design of the headquarters of the Center of Excellence is underway. It will serve as a testbed for the new technology.

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